sparsity

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Trainable models and NN optimization tools

Network Slimming (Pytorch) (ICCV 2017)

Neural Network Compression Framework for enhanced OpenVINO™ inference

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

Caffe for Sparse and Low-rank Deep Neural Networks

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

Caffe for Sparse Convolutional Neural Network

Sparse and structured neural attention mechanisms

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

Sparse Optimisation Research Code

[CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

A research library for pytorch-based neural network pruning, compression, and more.

OpenVINO Training Extensions Object Detection

Sparse Recurrent Neural Networks -- Pruning Connections and Hidden Sizes (TensorFlow)

Soft Threshold Weight Reparameterization for Learnable Sparsity

Ordered Weighted L1 regularization for classification and regression in Python

A package for AFM image reconstruction and compressed sensing in general

Unsupervised Machine Learning: Nonnegative Tensor Factorization + k-means clustering

A Library for Denoising Single-Cell Data with Random Matrix Theory

Attention-Based Guided Structured Sparsity of Deep Neural Networks

Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017

Implementation of the sparse icp algorithm

[ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.

Official PyTorch (Lightning) implementation of the NeurIPS 2020 paper "Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity".

[ICLR 2022] "Sparsity Winning Twice: Better Robust Generalization from More Efficient Training" by Tianlong Chen*, Zhenyu Zhang*, Pengjun Wang*, Santosh Balachandra*, Haoyu Ma*, Zehao Wang, Zhangyang Wang

Robust Single Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features